Deployment DevOps Engineer

📋 Description

• • Architect and own the end-to-end deployment lifecycle of Adaptive Engine, our flagship reinforcement-learning platform, ensuring it can be rolled out on-prem, in any major cloud (AWS, Azure, GCP), or as a managed SaaS with one-click simplicity.
• • Design and maintain resilient, version-controlled Kubernetes workflows (Helm charts, ArgoCD pipelines, GitOps) that scale from single-GPU proof-of-concepts to thousand-GPU production clusters handling trillions of user-interaction records.
• • Act as the first line of defense for customer escalations across North-American time zones: join live troubleshooting calls with Fortune-500 clients, reproduce issues in staging, coordinate hot-fixes, and feed learnings back into the product roadmap.
• • Build hardened, auditable deployment blueprints that satisfy enterprise security and compliance mandates—think OIDC/SSO integration, network micro-segmentation, WAF rules, secrets rotation, and automated CIS-hardened images.
• • Partner with Sales and Customer Success to deliver pre-sales demos, POC environments, and post-sales production cut-overs; you’ll often be the technical face of Adaptive ML during critical onboarding milestones.
• • Establish 24×7 on-call rotation, incident-response runbooks, and observability dashboards (Prometheus, Grafana, Loki) that keep MTTR under 15 minutes for P1 issues and provide clear RCA reports to executives.
• • Optimize data pipelines for multi-terabyte Postgres clusters and object storage, ensuring sub-second query latencies for real-time personalization while keeping storage costs predictable.
• • Champion Infrastructure-as-Code best practices (Terraform, Pulumi) so every environment—from dev to prod—can be recreated identically in minutes, not days.
• • Contribute code (Rust preferred) to internal tooling that automates certificate issuance, GPU driver updates, and chaos-engineering experiments that validate fault tolerance.
• • Influence the product roadmap by translating field feedback into concrete DevOps features: e.g., self-healing clusters, zero-downtime upgrades, or cost-aware auto-scaling policies.
• • Foster a culture of blameless post-mortems, continuous learning, and documentation that allows new team members to deploy safely on day one.
• • Mentor junior engineers and create reusable modules that accelerate future customer deployments, turning one-off fixes into scalable product capabilities.